Title: Research on consumer satisfaction prediction of e-commerce social platforms based on deep transfer learning

Authors: Hehua Mao

Addresses: Department of Economic Management, Shazhou Professional Institute of Technology, Zhangjiagang, Jiangsu, 215600, China

Abstract: Consumer satisfaction prediction can help e-commerce social platforms adjust their marketing plans, so this article proposes a consumer satisfaction prediction method of e-commerce social platforms based on deep transfer learning. Firstly, product reviews were collected from consumer on e-commerce social platforms, the value of each keyword through the TF-IDF method was calculated, and product characteristics were determined. Then, consumer emotions are divided, and a deep transfer learning model is used to eliminate false comments. Finally, taking the overall emotional value of each comment as a dependent variable and the characteristic emotional value as an independent variable, a multiple regression model for predicting consumer satisfaction is established to obtain relevant results. The experimental results show that the accuracy of the segmentation of consumer sentiment based on this method can reach 96.02%, and the accuracy of consumer satisfaction prediction can reach 0.90. The prediction effect is good.

Keywords: deep transfer learning; e-commerce social platforms; consumer; satisfaction prediction; TF-IDF method; multiple regression model.

DOI: 10.1504/IJNVO.2023.135948

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.229 - 243

Received: 24 Mar 2023
Accepted: 12 Jun 2023
Published online: 10 Jan 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article